Cognitive and adaptive telemetry

ABSTRACT

A telemetry master (TM) system provides automatic monitoring and maintenance of equipment by automatically generating predictive actions of a certain confidence threshold to backend systems which maintain a full duplex communication channel with the TM system. Data entries including context information that are generated by applications corresponding to the backend systems during the functioning of the backend systems are collected, analyzed and actionable items such as alerts, alarms of particular messages cause the telemetry master system to generate a predictive action of a minimum confidence threshold using a pre-trained first data model. A second data model is employed to identify particular equipment from the backend systems that is to implement the action. Results from implementing the action are collected and again used to train the data models. Certain applications can include intelligent agents which enable automatic execution of the actions.

PRIORITY

The present application claims priority under 35 U.S.C. 119(a)-(d) tothe Indian Provisional Patent Application Serial No. 201711024556,having a filing date of Jul. 12, 2017, the disclosure of which is herebyincorporated by reference in its entirety.

BACKGROUND

The ubiquitous presence of computers in almost every home, office,factory or other entities has led to availability of enormous quantitiesof data covering almost every aspect of life. In addition to historicaldata, real-time data is continuously produced via various electronicitems such as but not limited to equipment in factories, automobile orother vehicular hardware, consumer cell phones, and the like. Huge datacenters having capacity to store and process enormous amounts of dataare also established by both businesses and by governments. Computerprograms are being developed to mine the large quantities of data tolearn about human and machine behaviors. Techniques such as supervised,unsupervised learning and the like are employed to train artificialintelligence (AI) components such as neural networks, support vectormachines (SVMs) and the like.

BRIEF DESCRIPTION OF DRAWINGS

Features of the present disclosure are illustrated by way of examplesshown in the following figures. In the following figures, like numeralsindicate like elements, in which:

FIG. 1 shows a block diagram of a telemetry master (TM) system inaccordance with one example.

FIG. 2 illustrates a block diagram that shows the details of the TMsystem in accordance with some examples.

FIG. 3 shows the details of the TM system incorporated into a system fora utility plant in accordance with one example.

FIG. 4A shows a flowchart that details a method of monitoring a systemsuch as the a utilities Cognitive and Adaptive Telemetry (U-CAT) systemin accordance with some disclosed examples.

FIG. 4B is a flowchart that details a method of generating automaticactions in response to received alerts in a (u-CAT) system in accordancewith one example.

FIG. 5 shows a flowchart that details a method of monitoring a systemand intelligently triggering events and actions within the system asexecuted by the TM system in accordance with one example.

FIG. 6 shows the home page for the (u-CAT) system in accordance with anexample.

FIGS. 7A and 7B respectively show displays that are generated by theplant safety monitoring application while monitoring a location inaccordance with one example.

FIG. 8 shows a display of a geographical location being monitored forexample, by unmanned aerial vehicles such as drones in accordance withone example.

FIG. 9 shows an asset monitoring display generated by an assetmonitoring application in accordance with one example.

FIGS. 10A and 10B illustrate displays generated by an application for acognitive virtual power plant in accordance with one example.

FIG. 11 is a display generated by the power trader in accordance withone example.

FIG. 12 shows a log of the messages exchanged between two applications,in accordance with one example.

FIG. 13 shows a public network of various members which may include,applications, hardware objects and human users in accordance with oneexample.

FIG. 14 is a block diagram of a master agent in accordance with oneexample.

FIG. 15 shows a user interface generated by the master agent inaccordance with one example.

FIG. 16 is a flowchart that details a method of subscription acceptanceby an agent member.

FIG. 17 illustrates a computer system that may be used to implement oneor more of the TM system or the master agent in accordance with oneexample.

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure isdescribed by referring mainly to examples thereof. In the followingdescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. It will be readilyapparent however that the present disclosure may be practiced withoutlimitation to these specific details. In other instances, some methodsand structures have not been described in detail so as not tounnecessarily obscure the present disclosure. Throughout the presentdisclosure, the terms “a” and “an” are intended to denote at least oneof a particular element. As used herein, the term “includes” meansincludes but not limited to, the term “including” means including butnot limited to. The term “based on” means based at least in part on.

According to one or more examples described herein, a telemetry master(TM) system maintains a central repository for a plurality ofapplications that correspond to a plurality of backend systems ofvarious types which are associated with a single entity. The centralrepository includes a data buffer that receives data entries whichinclude routine updates regarding working of the different backendsystem. The data entries received at the central repository can alsoinclude alerts, alarms or other system-generated or manual messages anddata. In an example, the alerts may be from one or more of a pluralityof backend systems of different types and may be associated with asingle entity. The TM system processes the data entries andautomatically initiates actions responsive to the alerts within one ormore of the backend systems. The central repository included in the TMsystem receives and stores the various alerts along with extracting andmaintaining context information associated with the alerts in a contextbuffer. Based at least on the attributes of the alerts and the contextinformation, a predictive model predicts a next best action to be takenin response to the alert. A TM system Action processor receives the dataregarding the action to be taken, and identifies one or moreapplications that are to be employed in executing the action.

The TM system has a corresponding application layer which includes aplurality of applications having application programming interfaces(APIs) that are configured with full duplex communication channel toregularly exchange the data entries with the TM system. The receiveddata entries are transformed into a form that can be processed by the TMsystem and deposited into the central repository. In an example, anapplication respectively corresponding to a backend system may beincluded. The applications may further include agents to carry outvarious tasks under the control of the TM system. The TM system and theapplication layer form a reusable platform that can be extended toestablishments in various fields which implement complex computingsystems that ingest and emit data of different types and which requirehuman input for executing tasks to control the computing systems.

When data entries from the plurality of applications are received at thedata buffer, duplicate entries if any which are similar to the receiveddata entries are deleted from the data buffer. As the plurality ofapplications exchange data via many processes the received data entriescan also relate to various processes. In an example, the processesassociated with the received data entries can be identified via processid tags associated therewith. The received data entries can be accessedby a logic buffer also included with the TM system. The logic bufferstores information related to the stream of data which is beingcurrently processed to be used for next best action. One or more ofattributes and context information is extracted from the data entry vianatural language processing (NLP) techniques. In an example, the contextinformation can be stored in the context buffer.

The attributes and the context information are provided to a pre-trainedfirst data model for producing an action that can be executed by atleast one of the plurality of backend systems. The pre-trained ML-basedfirst data model can receive one or more of supervised or unsupervisedtraining on historical data for identifying the action to be executed.The historical data can include manual entries made by earlier usersregarding various updates, alerts, alarms or other issues, the variousactions that were executed in response to the updates, alerts, alarms orother issues and the results of implementing the various actions in atleast one of the plurality of backend systems.

Feature sets corresponding to the data entries can be set up by the TMsystem. A minimum number of data entries, corresponding to each of thedifferent processes for example, may need to be received at the databuffer prior to setting up the feature sets. The feature sets used tobuild a feature matrix and based on further processing of the featurematrix, the action to be executed is determined. In an example, a levelof confidence can be obtained from the feature matrix by employingtechniques such as but not limited to a softmax function. The confidencelevel thus obtained can be compared to a predetermined confidencethreshold. The action can be caused to be executed based on whether theconfidence level can clear the confidence threshold. If the action doesnot meet the confidence threshold, the received data entries willcontinue to be processed by the first data model as detailed hereinuntil an action that clears the confidence threshold is identified andimplemented.

The attributes and the context information of the data items can befurther provided to a second ML-based data model for identifyingequipment that will execute the action. In an example, the first datamodel can be a deep learning neural network. In an example, the seconddata model can be a long short-term memory (LSTM). The identifiedequipment is caused to execute the action and results from the executionof the action are recorded in the data buffer of the context repository.In an example, the results from the execution of the action can bedetermined based on analysis of the data entries that are constantlyrecorded by the TM system in the central repository. The data entriescan be analyzed using NLP techniques as detailed herein for determiningif the action achieved a purpose. If yes, then the data entriesassociated with the executed action can be loaded into a learnt bufferand provided to the first data model for training thereby improving theaccuracy of the first data model.

An example is disclosed wherein the TM system and the application layerare implemented in a power generating unit within a power utilityestablishment. Various tasks, such as but not limited to tracking healthof assets, running a power plant, monitoring the amount of powerproduced, determining the sufficiency of the power produced, andadvising further actions if insufficient power is produced, can beexecuted by the TM system in conjunction with various applications inthe application layer. In an example, the applications for the utilityestablishment may include but are not limited to applications forinterfacing with control systems, monitoring the functioning of thepower plant, applications for determining the sufficiency of thegenerated power, applications for addressing a power deficit ifinsufficient power is generated, and applications to interface with thecustomers of the power plant.

Each of these applications may interface with the backend systemsprocessing data of different data types to receive alerts or messagesfrom the backend systems. The applications employ APIs from varioustechnologies such as but not limited to machine learning (ML),artificial intelligence (AI), natural language processing (NLP), imageprocessing, data science and the like to process data from the backendsystems which may include a variety of data sources such as but notlimited to SCADA devices, smart sensors, ERP, SCM or CRM applicationpackages and the like. The APIs help implement solutions usingblockchain, distributed ledger, smart contract, automation,cryptocurrency and the like.

Each of the applications interfacing with the backend systems mayproduce a user interface that enables the human personnel of the powerplants to monitor the functioning of the power plant also in addition tomonitoring the tasks executed by the TM system and the applicationlayer. When and where required, the application layer provides for humanintervention via various user-selectable controls. The implementationdescribed herein enables advantages such as, but not limited to, analways “ON” utility, always “ON” workforce, automated trader to buypower at the least cost, reduced outages due to power deficits,maximizing the usage of AI and automation systems which optimize datausage by gleaning trends and implementing solutions based on the trendsand reducing bad debts and late payments among the customers of thepower utility establishment. The TM-application layer implementationenables applications such as cognitive virtual power plant and energyefficient advisor to optimize generation of power based on dynamicallyaltering demand-supply equilibrium. Applications such as dronemonitoring and image analysis improve efficiency in monitoring and otherautomated operations while power trader application enables the powerutility establishment to buy optimal amount of power at the least cost.The increase in overall efficiency in turn translates to reduced cost tocustomers and reduced customer churn.

A master agent can be included in the application layer for monitoringand maintaining a network of various intelligent agents of differentmember types which may include, for example, bots and human users. Thenetwork may be a private network as implemented in the TM-applicationlayer systems. However, in some examples, the network may be a publicnetwork wherein the master agent functions as a utility foradministering the various aspects of the public network. The masteragent includes APIs that enable the agents including one or more of thebots and humans (via their client devices) to register on the networkand subscribe to receive events, updates or other data that is exchangedbetween members of the network. Different APIs may be adopted to enablemembership of different member types. For example, human users can signup to the network or execute other actions on the network via userinterfaces (UIs) while the agents may execute the actionsprogrammatically. An integration platform such as Dell boomi enablesconfiguring the APIs to build the network and the master agent.

The master agent not only enables subscription services between membersbut also enables members to establish proxies to handle communicationswhen a particular member is off the network due to malfunction, networkoutage or other reason. The proxy service provides for a failovermechanism thereby ensuring continuity of services by the agents whichmay be servicing other network members. The master agent also providesratings for members which enable other members to determine whether ornot to sign up to receive a member's events. The network facilitatescommunications between agent members which may include many-to-one oreven one-to-one communication. For example, an agent may select anotheragent with specific criteria such as but not limited to, the shortesttime to response, geographically nearest agent or agent with a minimumrating and the like to receive a communication.

FIG. 1 shows a block diagram of the TM system 100 in accordance with oneexample. The TM system 100 provides for a centralized intelligentmonitoring and action agent in a large entity such as industrial orcommercial set up that includes a plurality of backend systems 132, 134,136 and the like. The various backend systems 132, 134 and 136, may eachhave its own database and application layer, user interfaces and thelike which may enable the backend system independent control of a subsetof functions needed for running the establishment. Examples of backendsystems for a commercial set up such as a power plant may include butare not limited to, power plant control systems, asset and safetymonitoring systems, energy usage monitoring systems, energy tradingsystems, human resource systems, customer care systems and financialsystems. Each backend system may be built using a specific technology,have its own APIs to output or to receive data input. Moreover thebackend systems 132, 134 and 136 may emit data of various types and invarious formats such as but not limited to geographical data such as GPSdata, graph/map data, geo-spatial data, events/alerts, unstructureddata, relational data and the like. Hence, in the absence of acentralized monitoring and action agent such as the TM system 100,valuable correlations that can be obtained by analyzing cross-functionaldata from different backend systems are lost.

The development of various technologies such as artificial intelligence(AI), data science, machine learning, natural language processing, imageanalysis techniques when combined with database technology that helpsstore large amounts of data emitted by each of the backend systems 132,134, 136 . . . enables gaining valuable insights into the workings ofthe power utility establishment besides developing intelligence toincrease the efficiency of the backend systems 132, 134 and 136.However, as each backend system 132, 134 or 136 has its own APIs anddata formats for communication, bridging elements are required so thatdata of different formats emitted from different systems may becorrelated. Accordingly, the TM system 100 is coupled to an applicationlayer 120 which includes a plurality of applications such as application1 122, application 2 124 and the like which can interface with thebackend systems 132, 134, 136. In an example, a respective applicationfrom the application layer 120 may be interfacing with each of thebackend systems 132, 134, 136. The applications 122, 124 etc. from theapplication layer 120 may receive the data from one or more of thebackend systems 132, 134, 136 and the like, convert the data to a commonformat and store it to the central repository 102 included in the TMsystem 100.

The central repository 102 can be a database that receives the variousmessages, alarms or alerts 1022 from each of the applications 122, 124 .. . from the application layer 120. Each of the applications 122, 124are configured with APIs to receive data from the backend systems 132,134, 136 in their respective native formats and convert the receiveddata to a uniform format that can be processed by the elements of the TMsystem 100. In an example, the TM system 100 includes a predictive model104 which predicts an action to be executed in response to receiving analert. In an example, the predictive model 104 can include neuralnetworks that are put through supervised training on past data of alarmsand actions that were taken in response to the alarms, conditionsassociated with the alerts, customer profiling data, customer-specificactions and the like. In an example, unsupervised learning techniques ortensor flow algorithm may be also be employed to train the predictivemodel 104. The predictive model 104 additionally considers the contextinformation 1024 that may also be stored in the central repository 102.The context information 1024 can include information that tracks themessages or alerts received along with their attributes or metadata suchas the application sending the alert, the date/time of the alert, thebackend system causing the alert, properties of the backend systems 132,134 etc. and the like. The context information 1024 can also include theactions initiated in response to the alerts, the applications andbackend systems involved in executing the actions, the response orresults obtained from the execution of the actions and any furtheraction that was taken or that is to be taken subsequent theresponse/results from the actions.

The information regarding the action identified by the predictive model104 is communicated to the TM Action processor 106. The action processor106 can implement a second ML-based data model such as a LSTM. Based onthe action to be executed, the TM Action processor 106 can identify theapplication (and hence, the backend systems 132, 134 . . . ) among theplurality of applications 122, 124 . . . which is to be selected forexecuting the action. In an example, the action can include a series ofsteps that are actually carried out by different applications. The TMAction processor 106 may identify such steps and the applications toexecute the actions. Accordingly, the triggers to initiate the actionsare transmitted to the corresponding applications. In an example, theapplications 122, 124, . . . may include one or more individual agents1220, 1240 . . . that execute scripts to carry out various functionssuch as raising alerts, exchanging messages, providing status updatesand the like. In an example, the application layer 120 may include amaster agent 150 to manage subscriptions between the agents as will bedetailed further herein.

When one or more of the applications 122, 124 . . . receive triggers toinitiate the actions, the various APIs to execute the actions arecalled. Further, the APIs to transmit the required data to theappropriate backend system(s) 132, 134, 136 are also called. Theappropriate backend system(s) execute the actions and again return theresults of the actions to the corresponding applications that triggeredthe actions. The applications in turn report the results of the actionsto the TM system 100. Based on the alerts/messages 1022 and the contextinformation 1024 in the central repository 102, the predictive model 104can determine if the actions were adequate in resolving a situation thatcaused the actions. If yes, the TM system 100 may transmit or store dataregarding the resolution of the action. If the situation is notresolved, then further action to resolve the situation may be identifiedby the predictive model 104 which can set into motion the proceduredescribed herein for the execution of the further action.

FIG. 2 illustrates a block diagram that shows the details of the TMSYSTEM 100 in accordance with some examples. As mentioned herein, the TMsystem 100 includes the central repository 102, the predictive model 104and the TM action processor 106. The central repository includes a databuffer 202, a context buffer 204 and a learnt buffer 206. The centralrepository 102 can also include a historical database 212 which storesarchived data related to the functioning of the plurality of backendsystems 132, 134 . . . prior to the being connected to the TM system100. For example, the archived data includes the various data entriesemitted by the plurality of backend systems 132, 134 . . . includingupdates, alerts, messages, alarms etc. in addition to includinginformation regarding the actions that were recommended eitherautomatically by the backend systems 132, 134 . . . or manually by theworkers, the recommended actions that were implemented, the results ofthe implemented actions and the like.

The data buffer 202 can be a temporary memory for holding the dataentries received by the TM SYSTEM 100 which are being processed inaccordance with examples disclosed herein. When a data entry isinitially received at the data buffer, a similarity checker 214 canimplement similarity checks based on techniques using similarityfunctions such as a cosine similarity for identifying if similar dataentry exists in the data buffer 202. If yes, then the earlier recordeddata entry can be deleted from the data buffer 202 while the laterreceived data entry is stored. In an example, both the data entries canbe recorded in the historical database 212.

For example, the data entries can have more than three features whichcan be represented graphically in a vector space which is a collectionof objects which include vectors. The vectors can be added togetherand/or multiplied or scaled by numbers i.e., scalars. From thedefinition of a dot product:v·w=∥v∥·∥w∥·cos θwhich can be re-arranged as:cos θ=v·w∥v∥·∥w∥

As the number of features will generally be greater, cosine similaritywhich is a pure vector based similarity calculation can be used in thevector space. The TM system 100 includes a data entry processer 216which can identify data patterns within the received data entries. Asmentioned herein the data entries from various backend systems 131, 134,. . . which may be in nonhomogeneous data format can be converted into acommon data format by a data entry processer 216. Based on the datapatterns within the processed data entries the context buffer 204 canimplement regular expressions to extract context information associatedwith each data entry.

When sufficient volume of a data pattern is formed, the TM SYSTEM 100proceeds to predict an action to be executed. The logic buffer 208accesses a data entry from the data buffer 202 to predict the action toexecute. A pre-trained first data model or the predictive model 104 canbe employed to predict the action to execute.

In an example, the predictive model 104 can include a deep learningneural network 222. Below is an example methodology of predicting anaction by the predictive model 104.

Let a training set include four data entries from three differentclasses (0, 1, and 2) which correspond to different actions that can beexecuted.

-   -   x₀→class 0    -   x₁→class 1    -   x₂→class 2    -   x₃→class 2

The class labels can be encoded into a format eg. one-hot encoding.

$\quad\begin{bmatrix}\left\lbrack 1. \right. & 0. & \left. 0. \right\rbrack \\\left\lbrack 0. \right. & 1. & \left. 0. \right\rbrack \\\left\lbrack 0. \right. & 0. & \left. 1. \right\rbrack \\\left\lbrack 0. \right. & 0. & \left. 1. \right\rbrack\end{bmatrix}$

A sample that belongs to class 0 can have the first row has one in thefirst cell, a sample that belongs to class 2 can have a 1 in the thirdcell of its row, and so forth. Next, a feature matrix of the fourtraining samples can be defined. Assuming, for example that the data sethas two features a 4×(2+1) dimensional matrix can be created (+1 for thebias term). Similarly, a (2+1)×3 dimensional weight matrix can becreated with one row per feature and one column for each class. In thiscase as the data set involves two features (Activity_ID, Cluster), inputcan be scaled from −3 to 3 so that all the features carry equalweightage. Accordingly:

${Inputs}\mspace{11mu} X{\text{:}\mspace{14mu}\begin{bmatrix}\left\lbrack 0.1 \right. & \left. 0.5 \right\rbrack \\\left\lbrack 1.1 \right. & \left. 2.3 \right\rbrack \\\left\lbrack {- 1.1} \right. & \left. {- 2.3} \right\rbrack \\\left\lbrack {- 1.5} \right. & \left. {- 2.5} \right\rbrack\end{bmatrix}}$${Weights}\mspace{14mu} W{\text{:}\mspace{14mu}\begin{bmatrix}\left\lbrack 0.1 \right. & 0.2 & \left. 0.3 \right\rbrack \\\left\lbrack 0.1 \right. & 0.2 & \left. 0.3 \right\rbrack\end{bmatrix}}$ ${bias}{\text{:}\mspace{14mu}\begin{bmatrix}0.01 & 0.1 & 0.1\end{bmatrix}}$$X\mspace{14mu}{{with}\mspace{14mu}}^{''}{ones}^{''}{\text{:}\mspace{14mu}\begin{bmatrix}\left\lbrack 1. \right. & 0.1 & \left. 0.5 \right\rbrack \\\left\lbrack 1. \right. & 1.1 & \left. 2.3 \right\rbrack \\\left\lbrack 1. \right. & {- 1.1} & \left. {- 2.3} \right\rbrack \\\left\lbrack 1. \right. & {- 1.5} & \left. {- 2.5} \right\rbrack\end{bmatrix}}$$W\mspace{14mu}{with}\mspace{14mu}{bias}{\text{:}\mspace{14mu}\begin{bmatrix}\left\lbrack 0.01 \right. & 0.1 & \left. 0.1 \right\rbrack \\\left\lbrack 0.1 \right. & 0.2 & \left. 0.3 \right\rbrack \\\left\lbrack 0.1 \right. & 0.2 & \left. 0.3 \right\rbrack\end{bmatrix}}$

To compute the net input the 4×(2+1) feature matrix X is multiplied withthe (2+1)×3 weight matrix W (n_features×n_classes).

Z=XW, which yields a 4×3 output matrix (n_samples×n_classes).

${net}\mspace{14mu}{{input}:\begin{bmatrix}\left\lbrack 0.07 \right. & 0.22 & \left. 0.28 \right\rbrack \\\left\lbrack 0.35 \right. & 0.78 & \left. 1.12 \right\rbrack \\\left\lbrack {- 0.33} \right. & {- 0.58} & \left. {- 0.92} \right\rbrack \\\left\lbrack {- 0.39} \right. & {- 0.7} & \left. {- 1.1} \right\rbrack\end{bmatrix}}$

At this point, softmax function can be used to compute the probability ythat a training sample x{circumflex over ( )}(i)$ belongs to class jgiven the weight and net input z{circumflex over ( )}(i). So, aprobability p(y=j|x{circumflex over ( )}(i); w_j) for each class labelin j=1, . . . , k can be computed. Note the normalization term in thedenominator which causes these class probabilities to sum up to one.Accordingly the next best action from the possible actions can beclassified.

${P\left( {y = {j❘z^{(i)}}} \right)} = {{\phi_{softmax}\left( z^{(i)} \right)} = {\frac{e^{z^{(i)}}}{\sum\limits_{j = 0}^{k}e^{z_{k}^{(i)}}}.}}$where net input z is defined as:

$z = {{{w_{0}x_{0}} + {w_{1}x_{1}} + {{.\;.\;.\;{+ \; w_{m}}}x_{m}}} = {{\sum\limits_{l = 0}^{M}{w_{i}x_{i}}} = {w^{T}{x.{softmax}}{\text{:}\mspace{14mu}\begin{bmatrix}\left\lbrack 0.29450637 \right. & 0.34216758 & \left. 0.36332605 \right\rbrack \\\left\lbrack 0.21290077 \right. & 0.32728332 & \left. 0.45981591 \right\rbrack \\\left\lbrack 0.42860913 \right. & 0.33380113 & \left. 0.23758974 \right\rbrack \\\left\lbrack 0.44941979 \right. & 0.32962558 & \left. 0.22095463 \right\rbrack\end{bmatrix}}}}}$

The values of each sample row as seen from above matrix, the values foreach sample (row) sum upto one. For example in the first sample,‘[0.29450637 0.34216758 0.36332605]’ has a 29.45% probability to belongto class 0. Now, in order to turn these probabilities back into classlabels, the argmax-index position of each row can be selected.

-   -   predicted class labels: [2200]

Predicted class labels are the action to be executed.

Now that the action to execute is determined, the TM action processor106 is next used to identify the equipment from one or more of thebackend systems 132, 134 . . . which is to execute the action. In anexample, the TM action processor 106 implements NLP for analysis of thedata entry to determine the equipment that will execute the action.

For example, a data entry ‘Get Cluster Data from Solar Park Monitoring’can be analyzed via regular expressions (Regex) implemented by thecontext buffer 204. Accordingly, context information related to a targetsystem ‘Solar Park Monitoring’ is extracted from the data entry. In thiscase LSTM sequence is implemented by the TM action processor 106 for theanalysis. Similarly, another data entry ‘Cluster 1 Area XYZ Tower 8 isaffected’ can be analyzed using regular expressions for extracting thecontext information ‘Cluster 1 Area XYZ Tower 8’. Again Regex with LSTMcan be implemented to identify that a cluster with context info ‘1’,area ‘XYZ’ and tower ‘8’ can be the equipment where the action is to beimplemented.

The TM action processor 106 implements a line by line sequence for theLSTM as this approach will allow the second data model (LSTM) or the TMaction processor 106 to use the context of each line in those caseswhere a simple one-word-in-and-out ambiguity. In this case this comes atthe cost of predicting words across lines which can be used if onlymodeling and generating lines of text is required. Note that in thisrepresentation, a padding of sequences is required to ensure that thesequences meet a fixed length input, which is a requirement when usingKeras.

First, the sequences of integers can be created, line by line using theTokenizer already fit on the source text.

# create line-based sequences sequences = list( ) for line indata.split(‘\n’): encoded = tokenizer.texts_to_sequences([line])[0] fori in range(1, len(encoded)): sequence = encoded[:i+1]sequences.append(sequence) print(‘Total Sequences: %d’ % len(sequences))

Next, the prepared sequences are padded using the pad_sequence( )function provided in Keras. This first involves finding the longestsequence, then using that as the length by which to pad-out all othersequences.

# pad input sequences max_length = max([len(seq) for seq in sequences])sequences = pad_sequences(sequences, maxlen=max_length, padding=‘pre’)print(‘Max Sequence Length: %d’ % max_length)

Next, the sequences are split into input and output elements as doneearlier.

# split into input and output elements sequences = array(sequences) X, y= sequences[:,:−1],sequences[:,−1] y = to_categorical(y,num_classes=vocab_size)

Once above steps are executed, the second data model corresponding tothe TM action processor 106 is defined and trained to give the equipmentthat is to execute the particular action that was determined by the deeplearning neural network.

The TM system 100 as described herein can be incorporated into largeestablishments such as various utility companies, pharmaceuticals,factories, mines or transportation facilities such as airports and thelike. Below is described an example of a utility system incorporatingthe TM system 100 or a utilities Cognitive and Adaptive Telemetry(U-CAT) system 300. FIG. 3 shows the details of the TM system 100incorporated into a system for a utility plant to implement actions. TheU-CAT system 300 is primarily made up of backend systems which includeservices that carry out market transactions 302, supervisory control anddata acquisition (SCADA) devices 304, smart sensors 306, intelligentdevices 308, and systems 310 for Enterprise Resource Planning (ERP),Supply Chain Management (SCM), Customer Relationship Management (CRM)and the like. The backend systems 302, 304, 306, 308 and 310 may produceand consume data of different formats including but not limited to,Graphs/Maps, geo-spatial, events/alerts, columnar, unstructured data andstructured data such as relational data.

The TM system 100 employs APIs of various technologies 330 including butnot limited to machine learning (ML), Artificial Intelligence (AI),natural language processing, natural language generation, imageprocessing and data science to correlate the various types of data toadminister the utility plant system efficiently thereby ensuring thatthe utility plant system runs smoothly. In order to be able to interfacewith the backend systems such as systems 302-310, a plurality of utilityplant applications 312-326 are developed. These include but are notlimited to, Energy Efficient Advisor 312, Drone Monitoring and ImageAnalytics 314, Cognitive Virtual Power Plant 316, Plant SafetyMonitoring 318, Intelligent Financial Recovery 320, Cognitive CustomerContact Center 322, Power Trader 324 and Asset Health Monitoring 336.Each of the utility plant applications 312-326 may employ one or more ofthe technologies 320 to execute actions in accordance with examplesdescribed herein. In an example, the utility plant applications 312-326may also include agents that execute scripts to carry out the variousfunctions.

An example of analysis of a data entry is discussed below. It can beappreciated that the below analysis and other details are discussed onlyfor illustration and that other analysis procedures can be adopted inaccordance with examples disclosed herein. The logic buffer 208initially pulls a data entry from the data buffer 202. An example of thedata entry accessed by the logic buffer 208 is shown below:

Solar Panel: Cluster 1 Damaged, Area XYZ impacted

The above data entry can be checked for Cosine similarity amongst thedata contained in the data buffer 202. Significantly matched entrieswill be removed from the data buffer 202. For example, the two dataentries shown below have a Cosine similarity of one and thus one of thedata entries corresponding to a prior entry can be removed from the databuffer 202.

‘Cluster 1 Damaged, Area XYZ impacted.’ ‘Cluster 1 Damaged, Area XYZimpacted.’

The context buffer 204 in the content repository 102 can storecontextual information from the above data entries.

Contextual Information: Cluster 1, Area XYZ

The data entry above can be used to generate a feature matrix and actionto be executed can be determined by the predictive model 104 by usingthe feature matrix. The TM action processor 106 determines the variouscomponents or equipment that will execute the action. Keeping the TMSYSTEM 100 computationally effective. In an example, the equipment toexecute the action is determined based on contextual informationprovided to the TM action processor 106 by the context buffer 204.

Newer data entries that are received at the data buffer after executionof the action at the one or more of the backend systems 132, 134 . . .will be provided to the logic buffer 208 and the above steps arerepeated. In an example, each process can have a corresponding processID tag to avoid data complexity. The data buffer 202 will continue toprovide samples and the new sample values are added to the featurematrix. The generated feature matrix can be used for prediction. Thesesteps are repeated till a decisive action that meets the confidencethreshold is determined. The feature matrix generated after determiningthe decisive action is transferred to the learnt buffer 206. After acertain number of records are gathered in the learnt buffer. Theserecords are sent to the train buffer 252. The predictive model 104 canbe retrained using the data that has been received to date at the databuffer 202.

In an example, the agents 1220, 1240 included in the differentapplications can subscribe to receive each other's notifications. Forexample, if a solar panel is damaged, the power deficit can beidentified and power can be automatically purchased based onsubscriptions exchanged by the agents included for example, within thecognitive virtual power plant 316 and the power trader 324.

FIG. 4A shows a flowchart 400 that details a method of monitoring asystem such as the a utilities Cognitive and Adaptive Telemetry (U-CAT)system 300 described above and intelligently triggering events andactions within the system as executed by the TM system 100. The methodbegins at 402 wherein an alert regarding a situation within the U-CATsystem 300 is received. Examples of different situations that cause thealerts and how the alerts are handled are discussed infra. The alert mayhave been generated by one of the applications 122, 124, . . . due to aroutine task, an anomaly, an automatic trigger or a human user actionwithin one of the backend systems 132, 134 . . . The alert from thebackend systems 132, 134 . . . is received by API of one of theapplications 122, 124 that is configured to handle the alerts from theparticular backend system. The application in turn logs the alert to thecentral repository 102 for processing by the TM system 100. Accordingly,at 404, the TM system 100 processes the alert and obtains the variousalert attributes which may include the context information 1024. Thecontext information 1024 can include application and/or the backendsystem generating the alert, the time, day and date of the alert,whether the alert was automatically generated or explicitly generated bya human user and the like. Based on the alert attributes, the TM system100 may identify an action at 406 that is to be executed in response tothe alert. At 408, an application for executing the action is selectedand a message for triggering the action is transmitted to the selectedapplication or the action is otherwise triggered automatically by the TMsystem 100 at 410. At 412, the results from the execution of the actionare received and it is determined at 414 if the action was an adequateresponse to the alert or if further action(s) are required. If it isdetermined at 414, that the action was an adequate response, theinformation such as the data entries associated with the action can betransferred to the learnt buffer 206 for training the predictive model104. Else the method returns to 406 to determine a further action to beexecuted.

FIG. 4B is a flowchart 450 that details a method of generating automaticactions in response to the received alerts in the (U-CAT) system 300.The method begins at 452 wherein an alert is received from the cognitivevirtual power plant 316 which monitors the demand-supply gaps arisingwithin the power utility establishment. The cognitive virtual powerplant 316 may be trained to project power supply adequacy based, forexample, on consumption and production patterns. Upon receiving thealert at 452, the TM system 100 may identify, for example, based oncontext information 1024, that a customer request action can beinitiated at 454. So a request can be sent to customers to curtail theirpower usage or to stagger their power usage from peak times to non-peakhours. The number of customers to contact may be determined based on theamount of power required to address the gap while the identities of thecustomers to contact can be obtained, for example, from the cognitivecustomer contact center 322 which has information regarding customerswho may sign up for the power savings program to obtain power at lowercost. The response from the customers is assessed at 456 to determine ifthe action initiated by the TM system 100, such as the customer requestaction at 454, adequately addresses the power deficit. In an example, ifsufficient number of customers to adequately fill the power deficitthrough altered usage patterns can be identified from the customerrequest action, it can be determined at 456 that the action identifiedat 454 was sufficient to address the power deficit. The TM system 100may send out a work order closing message at 464 indicating that thealert is resolved and the method terminates on the end block.

If it is determined at 456 that the action at 454 does not adequatelyaddress the power deficit or power shortage the method proceeds to 458to select a next action such as, to automatically buy power in view ofthe context information 1024 that a customer request action was earlierinitiated in response to the alert. The action at 454 may not adequatelyaddress the power deficit if only a subset of the customers who werecontacted respond in the affirmative to the request to curtail orstagger their power usage. In order to execute the action to buy power,the TM system 100 can automatically send instructions at 460 to thepower trader 324 to buy enough power to address the power deficit thatis still left over after the restricted power usage by the customersresponsive to the customer request action at 454. It can be appreciatedthat the automatic actions described herein are programmaticallyexecuted by the TM system 100 based on supervised/unsupervised trainingtechniques.

In an example, the amount of power to be purchased may be determined bythe cognitive virtual power plant 316 and communicated to the powertrader 324 via the TM system 100. Upon receiving a message at 462confirming the purchase of power, the TM system 100 may send out aclosing message at 464 indicating that the issue with the power deficitalert is resolved and the method may terminate on the end block withoutcirculating back to 456 to determine if the action initiated by the TMsystem 100 adequately addresses the power deficit.

FIG. 5 is a flow chart 500 that details a method of training apredictive model 104 to predict an action to be executed in accordancewith examples disclosed herein. The method begins at 502 wherein dataentries from a plurality of applications 122, 124 are received at thedata buffer 202. Data stored in the data buffer 202 can relate tomultiple processes or single process executing within one or more of theapplications 122, 124. In an example, similarities between prior dataentries stores in the data buffer and the data entries received at 502can be estimated and prior entries that are similar to the receivedentries can be deleted from the data buffer 202. The data entries areaccessed at 504 from the data buffer by the logic buffer 208. The dataentries are provided to the predictive model 104 and a prediction of anaction to be executed is obtained at 506. In an example, the dataentries can include corresponding process IDs so that the data entriesare provided to the predictive model 104 upon accumulation of sufficientpredefined number of data entries in the data buffer 202.

At 508 one of the backend systems 132, 134 . . . is enabled to implementthe action predicted at 506. The results from the implementation of theaction are received at 510 via new data entries arriving at the databuffer 202 subsequent to the implementation of the action at one of thebackend systems 132, 134 . . . . The new data entries are again providedat 512 to the predictive model 104 for predicting another action to beimplemented. In an example the new data entries are again checked forsimilarities with prior data entries and significantly matching priorentries are removed from the data buffer 202. The logic buffer 208continues to provide the new data entries to the predictive model 104until the other action is determined with confidence (e.g.,probabilities) that meets a predetermined confidence threshold.Accordingly it is determined at 514 if an action that meets theconfidence threshold is identified.

Upon identifying the action which meets the confidence threshold, thelogic buffer 208 will store the new data entries in a learnt buffer 206at 516 and the logic buffer 208 is refreshed. After a certainpredetermined number of data entries which meet a predefined datathreshold are gathered in the learnt buffer 206, these data entries aresent to the train buffer 252 at 518. The TM action processor 106 isemployed at 520 to identify equipment from the backend systems 132, 134. . . that will implement the other action. If at 514, it is determinedthat the action does not meet the confidence threshold then the logicbuffer 208 continues to provide the new data entries to the predictivemodel 104 until the other action which meets the confidence threshold isidentified. Therefore the method returns to 512 to provide the new dataentries to the predictive model 104.

FIG. 6 shows the home page 600 for the (U-CAT) system 200. The home page600 provides respective links 602, 604, 606, 608, 612, 614, 616, 618 toeach of the eight applications 312-326 included in the u-CAT system 300.The home page 600 also includes data 620 regarding the efficiencyobtained from each of the applications 312-326 by implementing the u-CATsystem 300. Also included, is the data 630 regarding the output obtainedfrom the applications 312-326. It can be appreciated that each of thefunctions shown under 630 such as power deficit averted, the customercomplaints resolved, dollars saved during power trading, plantintrusions detected, dollars averted in late payments, megawatts ofenergy usage reduced, number of energy efficient customers achieved,monitoring of the solar park, reprioritizing the work orders andmonitoring of electricity lines are automatically achieved by the TMsystem 100 implemented with the u-CAT system 300.

FIGS. 7A and 7B respectively show displays 700 and 750 that aregenerated by the plant safety monitoring 318 while monitoring alocation. The display 700 may be accessed, for example, via clicking theplant safety monitoring link 602. In an example, if the plant safetymonitoring 318 is employed for multiple sites, a plurality of selectabledisplays may be shown. The display 700 indicates via a marker 702 inreal-time, a location of a safety violation within one of the monitoredlocations. When selected by the user, the marker 702 generates a videodisplay 704 of the safety violation as it is occurring along with adescription 706 of the safety violation. A ticker tape 708 of theviolations occurring in the monitored locations is also generated.

FIG. 7B shows the display 750 of another safety violation occurring at adifferent spot within the monitored location. The display 750 may beaccessed by selecting another marker 752.

FIG. 8 shows another display 800 of a geographical location beingmonitored for example, by unmanned aerial vehicles such as drones. Thedrones may be administered by an application for automated dronemonitoring and image analytics 314. The automated drone monitoring andimage analytics 314 receives output from cameras on the drones togenerate the display 800 which also includes markers 802, 804 whereinselection of the marker 802 generates a display 810 which shows a videoor an image of a probable physical damage such as a broken solar panel,the likelihood of the physical damage and the accuracy with which thelocation of the probable physical damage is identified. Similarly,selection of the marker 804 may display another physical damage at adifferent physical location within an area monitored by the drones.

The displays 700, 750, and 800 are generated by applying image analysistechniques and classifiers trained to differentiate between images sothat anomalies such as safety violations or physical damages to assetscan be recognized and bought to the attention of appropriate personnel.In the case of a person without a safety helmet, image classifiers maybe trained on images of people with helmets and without helmets in orderto automatically identify the differentiation. Similar safety violationssuch as lack of proper uniform, suits, masks and the like may beidentified. Anomalies may also be identified via data obtained fromsensors on the drones which may include heat sensors, pressure sensors,light sensors, gas sensors, humidity sensors or other sensors. Thesignals emitted by the sensors are received by the correspondingapplications such as for plant safety monitoring 318, or automated dronemonitoring and image analytics 314. The APIs employed by the plantsafety monitoring 318, or automated drone monitoring and image analytics314 applications are configured to generate alerts that are capable ofbeing logged to the central repository 102 and processed by one or moreof the predictive model 104 or the TM Action processor 106.

Again, as mentioned herein the agents associated with the applicationsfor the asset monitoring 326, cognitive virtual power plant 316 and thepower trader 324 can exchange notifications via the TM system 100.Therefore, when a breakage of a solar panel is determined by the assetmonitoring 326 application, an interruption of power supply or a powerdeficit that can be expected may be projected by the virtual power plant316. The virtual power plant 316 can further communicate with the powertrader 324 via respective agents to purchase power that can compensatefor the power deficit projected due to the broken solar panel.Therefore, even when assets are not performing to capacity, theinterconnections between the various applications in the U-CAT system300 can lead to uninterrupted power supply.

FIG. 9 shows an asset monitoring display 900 generated by an applicationfor asset monitoring 326. It includes data for current work orders 902,current crew locations 904, current weather conditions 906 at a selectedlocation 908. In addition, it can include a ticker tape 910 which showswork orders that were automatically prioritized by the TM system 100based on the data from the sensors at the location 909, the currentweather conditions 906 and the context information 1024.

FIG. 10A illustrate an energy forecasting display 1000 generated by anapplication for a cognitive virtual power plant 316 which providesreal-time predictions regarding how one or more power supply sourcestrack their respective current demands. The energy forecasting display1000 shows a supply gap or deficit at 1002.

FIG. 10B shows another display 1050 generated by the cognitive virtualpower plant 316 wherein a message to buy power is successfullytransmitted as conveyed by the pop up 1052 when a send message button1054 was manually operated. In addition, the ticker tape 1060 showsstatuses of the various substations such as sending a demand-response(DR) alert for substation 2 or buying power for substation 5. The u-CATsystem 200 therefore enables actions such as purchasing of power to beexecuted either programmatically by the power trader 324 or manually bya human user.

FIG. 11 is a display 1100 generated by the power trader 324 wherein atransaction to buy power for substation 2 is initiated.

FIG. 12 shows a log 1200 of the messages exchanged between the powertrader application and the TM system 100 respectively during a powerpurchase operation that is programmatically initiated. As seen from thelog 1200, the power trader 324 application receives instructions fromthe TM system 100 to buy power. When the power trader 324 completes thetransaction, a message is passed on by the power trader application tothe TM system 100 confirming the purchase transaction. Furthermore, asmentioned earlier with respect to FIG. 3, the TM system 100 may furthercause the application for the cognitive Virtual power plant 316 todetermine if the purchase of power is adequate by requesting dataregarding power deficits as shown at 1206.

As mentioned herein, the application layer 120 includes variousintelligent agents 1220, 1240 associated with different applications.The agents 1220, 1240 and the like may be programmed to access variousAPIs to carry out the functions of the platforms such as the u-CATsystem 200 with which they may be associated. In an example, theapplication layer 120 may include the master agent 150 which isconfigured to enable exchange of communications between the agentsassociated with an application such as the U-CAT system 300. In anexample, the master agent 150 can enable agents to exchangecommunications over a network. In the examples described above, themaster agent 150 can facilitate agents 1220 and 1240 to subscribe toeach other's notifications. Accordingly, conversations such as thoseshown in 1200 can be automatically implemented. In an example, an agentmay be a program acting on behalf of a user or another program toexecute functions that simulates human activity. Agents may also includehardware elements such as smart sensors or other smart appliances thatcan be made accessible as independent elements on a network.

In an example, the master agent 150 may also be configured as anindependent network administering utility controlling functions of apublic network of various types of members which may include, not onlyagents acting on behalf of other human users and programs but also otherapplications, hardware objects and human users. FIG. 13 shows a publicnetwork 1300 of various members which may include, not only agents 1314,1316 acting on behalf of other human users and programs but also otherapplications, hardware objects 1318 and human users 1310, 1312. For easeof description and brevity, the members of the network 1300 will bedivided into agent members which may include agents, applications,hardware elements and the like and human members. The hardware elementscan include without limitation sensors, smart electronic/electricdevices, large and small industrial machinery or other elements whichare configured with communication interfaces that enable the elements tocommunicate on the agent network. The network 1300 therefore enablesconnecting personal appliances to commercial machinery or networks.

Each member of the network 1300 may have a unique network id thatenables other members to initiate communication with the member. For theagents on the network, communication is enabled via, for example, an IP(Internet Protocol) address on the network 1300. A human user may bereached via the member's unique network id which may be linked to themember's communication channel such as a cellular phone or an email orother channels on which the human user can receive messages. While theagent members 1314, 1316, 1318 may be part of the network 1300, it canbe appreciated that at least a subset of the interactions of the agentmembers 1314, 1316 and 1318 may be controlled by the humanadministrators that are responsible for connecting the agents 1314,1316, 1318 to the network. In an example, the human members 1310, 1312and the agent members 1314, 1316, 1318 may subscribe to receive eventsemitted by the other members. The agent members 1314, 1316, 1318 may beconfigured with limited autonomies or automatic functions on the network1300 wherein subscriptions from other network members may beautomatically accepted regardless of whether the subscribing member is ahuman or agent member, if the subscribing member satisfies certainauthentication criteria. The authentication criteria may be verified bythe master agent 150 and included with the subscription information. Insome examples, the agent members 1314, 1316, 1318 may require theirrespective human administrators to accept or reject subscriptions thatthey receive from other network members. If the network 1300 is a publicnetwork, any agent or human user who has capabilities or communicationchannels to exchange communications with other members may sign up formembership on the network 1300.

When a subscription is accepted by a member of the network 1300, theevents or information emitted by the member may be pushed to thesubscribing members. For example, some of the agent members 1314, 1316,1318 may be sensors or applications detecting or monitoring weather orother local conditions of geographical locations. Then other networkmembers may subscribe to the sensors or applications to obtain thelatest updates on the weather conditions, natural calamities such asfloods, fires, earthquakes and the like within the monitoring areas ofthe agents. In an example, the members of the network 1300 may exchangemessages in one-to-one format or many-to-one format.

FIG. 14 is a block diagram of the master agent 150. In an example, theelements of the master agent 150 may be an independent networkadministering utility controlling functions of the network 1300. Themaster agent 150 may include subscription APIs 1402 for managingsubscriptions of the network members 1310, 1312, 1314, 1316 and 1318,APIs for proxy service 1404 which enable proxies that can act on behalfof an agent, APIs for intelligent forwarding of requests 1406 to theappropriate members and optionally APIs for agent administration 1408.Accordingly, the master agent 150 may enable the agents to exchangemessages, subscribe to events emitted by other agents on the network1300, enable proxies that can act on behalf of an agent to preventbusiness disruptions and intelligent forwarding of requests to the rightagents. Agent administration can include o functions such as maintaininginformation regarding the agent members, enabling agents to connect toeach other via APIs on integration platforms such as Dell boomi and thelike. The agent master agent 150 also includes APIs for agentadministration 1408 which enables identifying and disabling spam agentsthat tend to flood the network 1300 with large amount of messages or spyagents that do not contribute to the communications on the network 1300except for collecting information from the network 1300 and the like.Similarly the master agent 150 may also include APIs to remove inactiveagents.

The master agent 150 may be coupled to an agent platform data store 1400which stores data required for the smooth functioning of the network1300. The agent platform data store 1400 stores member profiles 1410including agent profiles 1412 and user profiles 1414. While certain datasuch as but not limited to, date of signing up to the network 1300, themembers subscribed to, the members who subscribed to the profiledmember, member ratings such as ratings of the member associated with themember profile and the like may be common to both the agent profiles1412 and the user profiles 1414, certain data may differentiate an agentmember from a human member. Agent profiles 1412 may contain data notincluded in the user profiles 1414 which may include but is not limitedto, the managing user for the agent member associated with the agentprofile, the IP address of the agent member used for receiving andsending messages, the description of services if any provided by theagent member, the member ratings of the agent member's services,information regarding the proxy agents which may include one or moreother agent members acting as proxies for the agent member and one ormore other agent members for which the profiled agent member acts as aproxy. In addition to the member profiles 1410, the agent platform datastore 1400 may also store the various events 1416 generated by themembers of the network 1300 in order to transmit the events to thesubscribing members.

FIG. 15 shows a user interface 1500 generated by the master agent 150 inaccordance with one example. The user interface 1500 shows the profile1502 of an agent member ‘TakashiCorporate’ which includes userattributes such as but not limited to the status 1512 currently set todo not disturb (DND), confirm subscription option 1514 and whosecommunication link 1504 takashi.jp/response may be used by the network1200 or the master agent 150 to communicate with TakashiCorporate. Theuser interface 1500 includes a geographical display area 1506 thatdisplays the agents currently located in geographic proximity to‘TakashiCorporate’. The geographical display area 1506 includesselectable icons for the agents currently proximate to‘TakashiCorporate’ and when an agent named TokyoGeoSensor 1508 isselected, the information regarding the selected agent is shown in theagent info area 1510 which includes information regarding theadministrative member 1516 of the agent and a rating 1518 of the agent.The subscriptions of TakashiCorporate are shown in the subscriptionsarea 1524 and an events area 1520 of the user interface 1500 includesevents emitted by the TakashiCorporate agent. A live feed area 1522shows the live feeds from the various members of the network 1200subscribed to by TakashiCorporate.

FIG. 16 is a flowchart 1600 that details a method of subscriptionacceptance by an agent member as implemented by the master agent 150within the network 1200 in accordance with an example. The method beginsat 1602 wherein a subscription alert is received for an agent member atthe master agent 150 by another member of the network 1200. Thesubscription may be received either from another agent member or a humanmember. In either case, the attributes of the subscriber such as but notlimited to, the identity of the subscriber, the origin data such asgeographic location and/or network address of the subscription alert,the rating of the subscriber, the date at which the subscriber beganmembership of the network 1200 and the like are verified at 1604. Basedon the results of the verification of the attributes, a determination ismade at 1606 whether the subscription request is to be automaticallyaccepted or not. In addition to the subscriber attributes, theattributes or preferences of the member receiving the subscription alertmay also be considered when making the decision at 1606. If it isdetermined that the subscription is not to be automatically accepted,the method proceeds to 1620 wherein manual acceptance of thesubscription request is obtained and the method proceeds to 1608.

If at 1606, it is determined that the request for subscription can beautomatically accepted, at 1608, it is further determined if a proxyrequest is received, for example, corresponding to the subscriptionalert. As mentioned herein, an agent that is functionally similar toanother agent or which can execute the functions of another agent mayact as proxy for the other agent when the other agent is malfunctioningor otherwise inactive. Accordingly at 1610, the attributes are comparedand if it is determined at 1612 that the agent receiving the proxyrequest can act as a proxy to the requesting agent, the proxy request isaccepted at 1622 and the method terminates on the end block. If it isdetermined at 1612 that the agent receiving the proxy request cannot actas a proxy to the requesting agent, the proxy request is rejected at1614 and the method terminates on the end block.

FIG. 17 illustrates a computer system 1700 that may be used to implementone or more of the TM system 100 and the master agent 150. The computersystem 1700 may include additional components not shown and that some ofthe components described may be removed and/or modified.

The computer system 1700 includes processor(s) 1702, such as a centralprocessing unit, ASIC or other type of processing circuit, input/outputdevices 1712, such as a display, mouse keyboard, etc., a networkinterface 1704, such as a Local Area Network (LAN), a wireless 802.16xLAN, a 3G or 4G mobile WAN or a WiMax WAN, and a computer-readablemedium 1706. Each of these components may be operatively coupled to abus 1708. The computer-readable medium 1706 may be any suitable mediumwhich participates in providing instructions to the processor(s) 1702for execution. For example, the computer-readable medium 1706 may benon-transitory or non-volatile medium, such as a magnetic disk orsolid-state non-volatile memory or volatile medium such as RAM. Theinstructions or modules stored on the computer-readable medium 1706 mayinclude machine readable instructions 1764 executed by the processor(s)1702 to perform the methods and functions for one or more of the TMsystem 100 master agent 150. The computer-readable medium 1706 may alsostore an operating system 1762, such as MAC OS, MS WINDOWS, UNIX, orLINUX. The operating system 1762 may be multi-user, multiprocessing,multitasking, multithreading, real-time and the like. For example,during runtime, the operating system 1762 is running and theinstructions 1764 are executed by the processor(s) 1702.

The computer system 1700 may include a data storage 1710, which mayinclude non-volatile data storage. The data storage 1710 stores any dataused by one or more of the TM system 100 master agent 150. The datastorage 1710 may be used to store real-time data or processed historicaldata which may be used by one or more of the TM system 100 or the masteragent 150.

The network interface 1704 connects the computer system 1700 to internalsystems for example, via a LAN. Also, the network interface 1704 mayconnect the computer system 1700 to the Internet. For example, thecomputer system 1700 may connect to web browsers and other externalapplications and systems via the network interface 1704.

What has been described and illustrated herein are examples of thedisclosure along with some variations. The terms, descriptions andfigures used herein are set forth by way of illustration only and arenot meant as limitations. Many variations are possible within the scopeof the disclosure, which is intended to be defined by the followingclaims, and their equivalents, in which all terms are meant in theirbroadest reasonable sense unless otherwise indicated.

What is claimed is:
 1. A telemetry master (TM) system for automaticmonitoring and maintenance comprising: at least one processor; and anon-transitory data storage storing machine readable instructions thatcause the at least one processor to: receive an alert at a data bufferof the TM system from an application of a plurality of applications, theplurality of applications are communicatively coupled to a plurality ofbackend systems; obtain attributes of the alert via natural languageprocessing (NLP); retrieve context information to be used with theattributes via the NLP; predict, using a pre-trained, machine learning(ML)-based first data model, an action to be executed in response to thealert, the action being identified based on the attributes of the alertand the context information; select, using a second ML-based data model,at least one of the plurality of applications for executing the action;automatically trigger the action in one or more of the plurality ofbackend systems using the selected application; receive, via at leastone of the plurality of applications, results from triggering the actionin the one or more backend systems; determine if further action isrequired based on the results from triggering the action; and initiatethe further action if it is determined that further action is required,else transmit a closing message if it is determined that no furtheraction is required.
 2. The telemetry master system of claim 1, thenon-transitory data storage storing machine readable instructions thatcause the at least one processor to: receive data entries related tofunctioning of the plurality of backend systems from the plurality ofapplications subsequent to execution of one or more of the action andthe further action.
 3. The telemetry master system of claim 2, thenon-transitory data storage storing machine readable instructions thatcause the at least one processor to: determine, via a similarityfunction, prior data entries in the data buffer that are similar to thereceived data entries.
 4. The telemetry master system of claim 3, thenon-transitory data storage storing machine readable instructions thatcause the at least one processor to: delete the prior data entries inthe data buffer that are similar to the received data entries; and storethe received data entries in the data buffer.
 5. The telemetry mastersystem of claim 2, the non-transitory data storage storing machinereadable instructions that cause the at least one processor to:determine if the received data entries stored in the data buffer meet adata threshold; and train the first data model on the received dataentries upon the received data entries meeting the data threshold. 6.The telemetry master system of claim 1, wherein the machine readableinstructions for predicting the action to be executed causes the atleast one processor to: collect historical data regarding anomaliesarising in the plurality of backend systems and solutions implementedfor the anomalies; and train the first data model on the historicaldata.
 7. The telemetry master system of claim 6, wherein the first datamodel is a deep learning neural network.
 8. The telemetry master systemof claim 7, wherein a softmax function is used for determining theaction from a plurality of possible actions.
 9. The telemetry mastersystem of claim 1, wherein the machine readable instructions forretrieving the context information causes the at least one processor to:initially storing the context information in a context buffer that ispowered by regular expressions.
 10. The telemetry master system of claim1, wherein the second data model is a Long Short-Term Memory (LSTM). 11.A method for automatic monitoring and maintenance comprising: receivingat a central repository, data entries from at least one of a pluralityof applications associated with a plurality of backend systems;identifying prior data entries in a data buffer similar to the receiveddata entries; deleting the prior data entries that are similar to thereceived data entries; extracting attributes and contextual informationfrom the received data entries; setting the attributes and thecontextual information in a feature matrix; providing the feature matrixto a pre-trained first ML data model upon receiving a threshold numberof data entries; obtaining, from processing of the feature matrix by thepre-trained first ML data model, predicted class labels corresponding toactions to be executed; determining if probabilities associated with thefeature matrix meet confidence thresholds corresponding to the actionsto be executed; if at least one of the probabilities meets acorresponding one of the confidence thresholds: determining, via asecond data model, one or more of the plurality of backend systems ofthe plurality of backend systems that are to execute the actions;causing the one or more of the plurality of backend systems to executethe actions; and if at least one of the probabilities does not meet thecorresponding one of the confidence thresholds: repeating the steps ofreceiving, identifying, deleting, extracting, setting, providing,obtaining and determining for the at least one probability that does notmeet the corresponding one of the confidence thresholds.
 12. The methodof claim 11, further comprising: processing the received data entriesvia natural language processing (NLP); and storing context informationassociated with the data entries in a context buffer.
 13. The method ofclaim 11, wherein determining via the second data model one or more ofthe plurality of backend systems further comprises: processing, usingregular expressions via a long short-term memory (LSTM), the receiveddata for determining the one or more backend systems of the plurality ofbackend systems that are to execute the actions.
 14. The method of claim13, wherein determining the one or more backend systems furthercomprises: extracting context information from the received data entriesusing the regular expressions.
 15. The method of claim 11, whereinidentifying prior data entries in a data buffer similar to the receiveddata entries further comprises: identifying the prior data entries thatare similar to the received data entries using cosine similarityfunction.
 16. The method of claim 11, further comprising: maintaining afull duplex communication channel between the central repository and theplurality of applications.
 17. The method of claim 16, wherein causingthe one or more of the plurality of backend systems to execute theactions further comprises: causing exchange of communications via thecommunication channel between the one or more of the plurality ofapplications.
 18. A non-transitory computer-readable storage mediumcomprising machine-readable instructions that cause a processor to:receive at a central repository, data entries from at least one of aplurality of applications associated with a plurality of backendsystems; identify prior data entries in a data buffer similar to thereceived data entries; delete the prior data entries that are similar tothe received data entries; extract attributes and contextual informationfrom the received data entries; set the attributes and the contextualinformation in a feature matrix; provide the feature matrix to apre-trained first ML data model upon receiving a threshold number ofdata entries; obtain, from processing of the feature matrix by thepre-trained first ML data model, predicted class labels corresponding toactions to be executed; determine if probabilities associated with thefeature matrix meet confidence thresholds corresponding to the actionsto be executed; if at least one of the probabilities meets acorresponding one of the confidence thresholds: determine, via a seconddata model, one or more of the plurality of backend systems of theplurality of backend systems that are to execute the actions; cause theone or more of the plurality of backend systems to execute the actions;and if at least one of the probabilities does not meet the correspondingone of the confidence thresholds: repeat the steps of receiving,identifying, deleting, extracting, setting, providing, obtaining anddetermining for the at least one probability that does not meet thecorresponding one of the confidence thresholds.
 19. The non-transitorycomputer-readable storage medium of claim 18, further comprisingmachine-readable instructions that cause the processor to: determine ifthe received data entries stored in the data buffer meet a datathreshold; and train the pre-trained first data model on the receiveddata entries upon the received data entries meeting the data threshold.20. The non-transitory computer-readable storage medium of claim 18wherein the plurality of applications include applications related to apower plant and the machine readable instructions for determining theaction to be executed comprise further instructions that cause theprocessor to: determine that a power deficit exists due to a damagedasset; and enable automatic purchase of additional power to cover thepower deficit.